**3. Malaysian economy, bond market development and major crises since 1996**

With the celebration of her 60 years of independence, Malaysia has been globally recognized not only for the recent and dedicated effort in the Islamic banking and finance development, but is also known for the multicultural and diverse ethnics, apart from being among the top ranks in terms of GDP growth within the South-East Asian countries. With a population reaching up to 30 million people, Malaysia has recorded a stable economic growth over the last 20 years, with the highest recorded at 10% in 1996. GDP per capita has also increased by 98% from USD4797 to USD9502 in 1996 and 2016, respectively<sup>5</sup> .

It is important to highlight that healthy growth of the local bond market is essential in order to support the mobility of funds in the financial system. During the past 20 years since 1996, the bond market6 in Malaysia has grown remarkably, with bond outstanding value recorded at RM1.17 trillion in 2016. The sovereign bond has also increased many-fold over the last two decades, from RM75 billion in 1998 to RM628 billion last year. This rapid growth of the bond market is one of the

<sup>5</sup> FRED Economic Data, Federal Reserve Bank of St. Louis.

<sup>6</sup> Comprises of sovereign and private debt securities, including sukuk (Islamic bonds), sourced from Securities Commission Malaysia (www.sc.com.my).

#### *Has the Yield Curve Accurately Predicted the Malaysian Economy in the Previous Two Decades? DOI: http://dx.doi.org/10.5772/intechopen.92214*

motivating factors to undertake the research apart from the interest to establish the long-run relation of the spread with the growth.

Nonetheless, with an open economy structure, Malaysia is not exempted from economic crisis, with two of the major crises occurred within the past 20 years. One of the most significant ones was the 1997 Asian financial crisis triggered by the Thai Baht speculative trading leading to the domino effect on all Asian countries, causing the Malaysian Ringgit to be heavily sold and depreciated by almost 50% in value by January 1998, see Athukorala [27]. Coupled with the internally induced banking crisis, massive short-term and un-hedged capital inflows and sudden reversal of capital outflows have exacerbated the situation leading the massive downturn of the economy. GDP growth was recorded at negative 7.4% in 1998 from a positive growth of 7.3% recorded just a year earlier. Given the negative growth, Malaysian economy was officially down with recession, with the number of retrenchments increasing from 19,000 in 1997 to over 83,000 in 1998, while inflation rate peaked at 6.2% in 1998 surpassing the previous peak of 5.3% in 1991, see Athukorala [27].

Another notable crisis is the global financial crisis that was initiated from the credit subprime mortgage market in the US in 2007, being one of the horrendous events that has ever occurred in the world's history. The innovation acts of financial engineering to securitize and increase the liquidity of the US subprime residential mortgage-backed securities<sup>7</sup> and packaged them into collateralized debt obligations (CDOs), had turned out to be an unimaginable crisis affecting not only the US economy but the rest of the world. The downturn impact arising from this crisis for Malaysia was evident when the growth was reduced from 6.5% in 2007 to 4.7% in 2008 with subsequent negative growth of 1.7% in 2009.

Mapping the movements of the yield spread over the past two decades, it is visible that the spread turned negative prior to the two recessions in Malaysia, as shown in **Figure 1** below. Whether the inversion of the yield curve could really signal for future declining of economic output should be proven through the

#### **Figure 1.**

*Malaysian yield spread and major recessions over 20 years. Note: The yield spread is calculated as the difference between the yields on 10-year and 3-month Malaysian Treasury securities. The shaded areas denote major recessions.*

<sup>7</sup> Subprime mortgages refer to mortagages made to borrowers who are less creditworthy than prime borrowers, see Dwyer and Tkac []. This study also presents an interesting series of events occurred during the crisis and how the tiny market of CDOs became the triggering factor.

empirical analysis undertaken to test the predictive ability of the yield curve in Malaysia, by establishing the long-run cointegration with economic growth. The following section will then discuss how the model is developed and how tests will be done on the collected data.

incorporate the leading macroeconomics indicators and expressed based on the

*Has the Yield Curve Accurately Predicted the Malaysian Economy in the Previous Two Decades?*

<sup>θ</sup><sup>t</sup> *<sup>Δ</sup>*Spreadt�<sup>i</sup> <sup>þ</sup><sup>X</sup>

where *lnYt* is the economic growth indicated by industrial production index and expressed in natural logarithm, Spreadt is the yield spread between 10-year government bond and 3-month Treasury bill and Leadingt are the controlled variables for other macroeconomics leading indicators, *Δ* is first-difference operator, and p is the optimal lag length whereby the optimal lag length which represents the previous values, are being automatically selected based on Akaike info criterion (AIC). In consideration that the growth could be serially correlated, since previous growth might influence future growth, its past values are useful predictors themselves. This could also be the case for other independent variables, namely spread

The estimation model above will be applied onto three different samples, first on the whole sample (sample A) for the period of January 1996 to December 2016, while the second and third samples are based on the periods within the occurrences of the major crisis, from January 1996 to December 2000 (sample B) and from January 2007 to December 2009 (sample C), respectively. Our aim is to examine whether the long-run relationship among the variables, particularly the significance of the yield spread in explaining growth, still persists over different time periods. The ARDL long-run form and bounds test is then undertaken for testing the existence of the long-run relationship, which is detected through the F-statistics (Wald test), and is said to be established if the F-statistics exceeds the critical value band, see Nkoro and Uko [32]. Specifically, the null hypothesis for no cointegration among variables in Eq. (2) is defined as H0: δ1 = δ2 = δ3 = 0 (where long-run relationship does not exist) against the alternative hypothesis of H1: δ1 6¼ δ2 6¼ δ3 6¼ 0 (long-run relationship does exist). Upon running the ARDL long-run form and bound test in Eviews 9.5, two sets of critical values are generated of which one set refers to I(0) and the other one refers to I(1). Critical values for the I(1) series are referred to as upper bound critical values, while the critical values for I(0) series are referred to lower bound critical values (Duasa, [31]). This is the bound testing procedure generated through the ARDL model and widely used in the estimation of long-run relationships when the properties of the time-series data are a mixture

If there is evidence of long-run relationship (cointegration), the following model

Subsequently, the ARDL specification of the short-run dynamics is derived by

<sup>θ</sup><sup>t</sup> *<sup>Δ</sup>*Spread <sup>þ</sup><sup>X</sup>

<sup>θ</sup><sup>t</sup> Spreadt�<sup>i</sup> <sup>þ</sup><sup>X</sup>

*p*

*i*¼0

*p*

*i*¼0

<sup>β</sup><sup>t</sup> Leadingt�<sup>i</sup> <sup>þ</sup> <sup>μ</sup><sup>t</sup> (3)

β<sup>t</sup> *Δ*Leading þ ψecmt þ ε<sup>t</sup> (4)

<sup>þ</sup> <sup>δ</sup><sup>2</sup> Spreadt�<sup>1</sup> <sup>þ</sup> <sup>δ</sup><sup>3</sup> Leadingt�<sup>1</sup> <sup>þ</sup> <sup>υ</sup><sup>t</sup> (2)

*p*

*i*¼0

β<sup>t</sup> *Δ*Leadingt�<sup>i</sup> þ δ<sup>1</sup> *lnY*<sup>t</sup>�<sup>1</sup>

ARDL framework:

*<sup>Δ</sup>lnYt* <sup>¼</sup> *<sup>α</sup><sup>0</sup>* <sup>þ</sup><sup>X</sup>

and leading.

of I(0) and I(1).

*lnYt* <sup>¼</sup> *<sup>α</sup><sup>1</sup>* <sup>þ</sup><sup>X</sup>

*<sup>Δ</sup>lnYt* <sup>¼</sup> *<sup>α</sup><sup>2</sup>* <sup>þ</sup><sup>X</sup>

**157**

*p*

<sup>φ</sup>*<sup>t</sup> lnYt*�*<sup>i</sup>* <sup>þ</sup><sup>X</sup>

<sup>φ</sup>*<sup>t</sup> <sup>Δ</sup>lnYt*�*<sup>i</sup>* <sup>þ</sup><sup>X</sup>

*p*

*i*¼0

constructing the error correction model (ECM) of the following form:

*p*

*i*¼0

where the ecmt is the error correction term and is defined as.

*i*¼0

*p*

*i*¼0

is estimated:

*p*

*DOI: http://dx.doi.org/10.5772/intechopen.92214*

<sup>φ</sup>*<sup>t</sup> <sup>Δ</sup>lnYt*�*<sup>i</sup>* <sup>þ</sup><sup>X</sup>

*p*

*i*¼0

*i*¼0
